IASL valence-arousal analysis system at IALP 2016 shared task: Dimensional sentiment analysis for Chinese words

2016 ◽  
Author(s):  
Yu-Lun Hsieh ◽  
Chen-Ann Wang ◽  
Ying-Wei Wu ◽  
Yung-Chun Chang ◽  
Wen-Lian Hsu
Keyword(s):  
Sentiment Analysis ◽  
Shared Task ◽  
Analysis System

scholarly journals Sentiment Analysis of Short Informal Texts

2014 ◽  
Vol 50 ◽  
pp. 723-762 ◽  
Author(s):  
S. Kiritchenko ◽  
X. Zhu ◽  
S. M. Mohammad
Keyword(s):  
Sentiment Analysis ◽  
Text Classification ◽  
State Of The Art ◽  
Surface Form ◽  
Shared Task ◽  
High Coverage ◽  
Percentage Points ◽  
Sentiment Lexicon ◽  
Analysis System ◽  
Performance Gains

We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the sentiment of a word or a phrase within a message (term-level task). The system is based on a supervised statistical text classification approach leveraging a variety of surface-form, semantic, and sentiment features. The sentiment features are primarily derived from novel high-coverage tweet-specific sentiment lexicons. These lexicons are automatically generated from tweets with sentiment-word hashtags and from tweets with emoticons. To adequately capture the sentiment of words in negated contexts, a separate sentiment lexicon is generated for negated words. The system ranked first in the SemEval-2013 shared task `Sentiment Analysis in Twitter' (Task 2), obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. Post-competition improvements boost the performance to an F-score of 70.45 (message-level task) and 89.50 (term-level task). The system also obtains state-of-the-art performance on two additional datasets: the SemEval-2013 SMS test set and a corpus of movie review excerpts. The ablation experiments demonstrate that the use of the automatically generated lexicons results in performance gains of up to 6.5 absolute percentage points.

scholarly journals Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

2021 ◽  
Author(s):  
Asad Khattak ◽  
Muhammad Zubair Asghar ◽  
Zain Ishaq ◽  
Waqas Haider Bangyal ◽  
Ibrahim A Hameed
Keyword(s):  
Sentiment Analysis ◽  
User Reviews ◽  
Analysis System

scholarly journals MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 115 ◽  
Author(s):  
Yaocheng Zhang ◽  
Wei Ren ◽  
Tianqing Zhu ◽  
Ehoche Faith
Keyword(s):  
Sentiment Analysis ◽  
Interaction Design ◽  
Mobile Application ◽  
Situational Awareness ◽  
Mobile Internet ◽  
The Public ◽  
Public Sentiment ◽  
High Altitude Area ◽  
Sentiment Dictionary ◽  
Analysis System

The development of mobile internet has led to a massive amount of data being generated from mobile devices daily, which has become a source for analyzing human behavior and trends in public sentiment. In this paper, we build a system called MoSa (Mobile Sentiment analysis) to analyze this data. In this system, sentiment analysis is used to analyze news comments on the THAAD (Terminal High Altitude Area Defense) event from Toutiao by employing algorithms to calculate the sentiment value of the comment. This paper is based on HowNet; after the comparison of different sentiment dictionaries, we discover that the method proposed in this paper, which use a mixed sentiment dictionary, has a higher accuracy rate in its analysis of comment sentiment tendency. We then statistically analyze the relevant attributes of the comments and their sentiment values and discover that the standard deviation of the comments’ sentiment value can quickly reflect sentiment changes among the public. Besides that, we also derive some special models from the data that can reflect some specific characteristics. We find that the intrinsic characteristics of situational awareness have implicit symmetry. By using our system, people can obtain some practical results to guide interaction design in applications including mobile Internet, social networks, and blockchain based crowdsourcing.

scholarly journals Statistical and Similarity Methods for Classifying Emotion in Suicide Notes

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8958 ◽  
Author(s):  
Kirk Roberts ◽  
Sanda M. Harabagiu
Keyword(s):  
Sentiment Analysis ◽  
Similarity Measures ◽  
Classification Problem ◽  
Shared Task ◽  
Suicide Notes ◽  
F Measure

In this paper we report on the approaches that we developed for the 2011 i2b2 Shared Task on Sentiment Analysis of Suicide Notes. We have cast the problem of detecting emotions in suicide notes as a supervised multi-label classification problem. Our classifiers use a variety of features based on (a) lexical indicators, (b) topic scores, and (c) similarity measures. Our best submission has a precision of 0.551, a recall of 0.485, and a F-measure of 0.516.

A Sentiment Analysis System to Improve Teaching and Learning

Computer ◽  
2017 ◽  
Vol 50 (5) ◽  
pp. 36-43 ◽  
Author(s):  
Sujata Rani ◽  
Parteek Kumar
Keyword(s):  
Sentiment Analysis ◽  
Teaching And Learning ◽  
Analysis System

scholarly journals Sentiment Analysis System in Big Data Environment

2018 ◽  
Vol 33 (3) ◽  
pp. 187-202
Author(s):  
Wint Nyein Chan ◽  
Thandar Thein
Keyword(s):  
Big Data ◽  
Sentiment Analysis ◽  
Data Environment ◽  
Analysis System
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